Introduction to HANA in-memory from SAP

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Introduction to HANA in-memory from SAP: What is HANA. In-memory technology from SAP.

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  • Business users of all levels are empowered to conduct immediate ad hoc data analyses and transaction processing using massive amounts of real time data for expanded business insight.It frees up IT resources and lowers the cost of operations.
  • Defining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)Right click  Data PreviewRight click  Activate: This action will activate the Attribute View with selected fields as key figures and associated measures.
  • We can also view distinct values in each of these fields and perform a quick analysis (data disbursement in graphical format) Analyzing the data present in an attribute: (By selecting Dimensions, Measures and applying filters) Also, we can change the type of chart we want to use depending on the type of data.
  • Creating Attribute Hierarchies: From the Attribute properties window  Click on Hierarchies Tab  Create New hierarchy  We can create two types here (Level Hierarchy and Parent Child hierarchy. Drag and Drop the attributes from the list available as shown:
  • We can create Analytic views from either a table imported into HANA or from Attribute Views that were createdOrBy duplicating existing views and further edit for a different purpose
  • The model of Attributes and Analytic View will appear as below after establishing the relationships:Activate the view by right clicking in the studioNow the Analytic View is ready to be accessed by the Explorer.
  • Introduction to HANA in-memory from SAP

    1. 1. Core Team:<br />xxx<br />Introduction to HANA<br />Manoj Ketha<br />NA SBO Competency Center<br />
    2. 2. Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    3. 3. In-Memory Computing <br />Technology that allows the processing of massive quantities of real time data in the main memory of the server to provide immediate results from analyses and transactions<br />
    4. 4. Vision: In-Memory Computing<br />Technology Constrained Business Outcome<br />Current Scenario<br />Sub-optimal execution speed<br />Lack of responsiveness due to data latency and deployment bottlenecks<br /><ul><li>Inability to update demand plan with greater than monthly frequency</li></ul>Lack of business transparency<br />Sales & Operations Planning based on subsets of highly aggregated information, being several days or weeks outdated.<br />Increasing Data Volumes<br />Information Latency<br />Calculation Speed<br />Type and # of Data Sources<br />Reactive business model<br />Missed opportunities and competitive disadvantage due to lack of speed and agility <br /><ul><li>Utilities: daily- or hour-based billing and consumption analysis/simulation. </li></li></ul><li>Vision: In-Memory Computing<br />Leapfrogging Current Technology Constraints<br />Future State<br />Flexible Real Time Analytics<br /><ul><li>Real-time customer profitability
    5. 5. Effective marketing campaign spend based on large-volume data analysis</li></ul>Improve Business Performance<br /><ul><li>IT rapidly delivering flexible solutions enabling business
    6. 6. Speed up billing and reconciliation cycles for complex goods manufacturers
    7. 7. Planning and simulation on the fly based on actual non-aggregated data</li></ul>TeraBytes of DataIn-Memory<br />100 GB/s data througput <br />RealTime<br />Freedom from the data source<br />Competitive AdvantageE.g. Utilities Industry:<br /><ul><li>Sales growth and market advantage from demand/cost driven pricing that optimizes multiple variables – consumption data, hourly energy price, weather forecast, etc.</li></li></ul><li>In-Memory Computing – The Time is NOWOrchestrating Technology Innovations<br />The elements of In-Memory computing are not new. However, dramatically improved hardware economics and technology innovations in software has now made it possible for SAP to deliver on its vision of the Real-Time Enterprise with In-Memory business applications<br />HW Technology Innovations<br />SAP SW Technology Innovations<br />Row and Column Store<br />Multi-Core Architecture (8 x 8core CPU per blade)<br />Massive parallel scaling with many blades<br />Compression<br />Partitioning<br />64bit address space – 2TB in current servers<br />100GB/s data throughput<br />Dramatic decline in price/performance<br />No Aggregate Tables<br />Real-Time Data Capture<br />Insert Only on Delta<br />
    8. 8. SAP Strategy for In-Memory<br />TECHNOLOGY INNOVATION  BUSINESS VALUE<br />Real-Time Analytics, Process Innovation, Lower TCO<br />HEART OF FUTURE APPLICATIONS<br />Packaged Business Solutions for Industry and Line of Business<br />CUSTOMER CO-INNOVATION<br />Design with customers<br />GUIDING PRINCIPLES<br />INNOVATION WITHOUT DISRUPTION<br />New Capabilities For Current Landscape<br />EXPAND PARTNER ECOSYSTEM<br />Partner-built applications, Hardware partners<br />
    9. 9. Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    10. 10. In-Memory Computing Product “SAP HANA”SAP High Performance Analytic Appliance<br />What is SAP HANA?<br />SAP HANA is a preconfigured out of the box Appliance<br /><ul><li>In-Memory software bundled with hardware delivered from the hardware partner (HP, IBM, CISCO, Fujitsu)
    11. 11. In-Memory Computing Engine
    12. 12. Tools for data modeling, data and life cycle management, security, operations, etc.
    13. 13. Real-time Data replication via Sybase Replication Server
    14. 14. Support for multiple interfaces
    15. 15. Content packages (Extractors and Data Models) introduced over time</li></ul>Capabilities Enabled<br /><ul><li>Analyze information in real-time at unprecedented speeds on large volumes of non-aggregated data.
    16. 16. Create flexible analytic models based on real-time and historic business data
    17. 17. Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category
    18. 18. Minimizes data duplication</li></ul>BI Clients<br />3rd Party<br />In-Memory<br />SQL<br />MDX<br />BICS<br />SAP HANA<br />SAP HANAmodeling<br />SAPBusiness<br />Suite<br />replicate<br />ETL<br />3rd Party<br />SAP BW<br />
    19. 19. Technical Overview<br />Calculation models – Extreme Performance and Flexibility with Calculations on the fly<br />Calculation Model<br /><ul><li>A calc model can be generated on the fly based on input script or SQL/MDX
    20. 20. A calc model can also define a parameterized calculation schema for highly optimized reuse
    21. 21. A calc model supports scripted operations</li></ul>SQL<br />MDX<br />SQL Script<br />Plan Model<br />other<br />In-Memory Computing Engine<br />Compile & Optimize<br />Parse<br />Calculation Model<br />Calculation Engine<br />Data Storage<br /><ul><li>Row Store - Metadata
    22. 22. Column Store – 10-20x Data Compression</li></ul>Logical Execution Plan<br />Distributed Execution Engine<br />Physical Execution Plan<br />Column Store<br />Row Store<br />
    23. 23. SAP BusinessObjects Data Services Platform<br />Rich Transforms<br />Integrate heterogeneous data into BWA<br />Integrated Data Quality<br />Text Analytics<br />Extract From Any Data Source into HANA<br />Syndicate From HANA to Any Consumer<br />© SAP 2007/Page 11<br />
    24. 24. SAP HANA Road Map:In-Memory Introduction <br />Today‘s System Landscape<br /><ul><li>ERP System running on traditional database
    25. 25. BW running on traditional database
    26. 26. Data extracted from ERP and loaded into BW
    27. 27. BWA accelerates analytic models
    28. 28. Analytic data consumed in BI or pulled to data marts</li></ul>Step 1 – In-Memory in parallel(Q4 2010)<br /><ul><li>Operational data in traditional database is replicated intomemory for operational reporting
    29. 29. Analytic models from production EDW can be brought into memory for agile modeling and reporting
    30. 30. Third party data (POS, CDR etc) can be brought into memory for agile modeling and reporting</li></li></ul><li>Step 2 – Primary Data Store for BW(Planned for Q3 2011)<br /><ul><li>In-Memory Computing used as primary persistence for BW
    31. 31. BW manages the analytic metadata and the EDW data provisioning processes
    32. 32. Detailed operational data replicated from applications is the basis for all processes
    33. 33. SAP HANA 1.5 will be able to provide the functionality of BWA</li></ul>Step 3 – New Applications (Planned for Q3 2011)<br /><ul><li>New applications extend the core business suite with new capabilities
    34. 34. New applications delegate data intense operations entirely to the in-memory computing
    35. 35. Operational data from new applications is immediately accessible for analytics – real real time</li></ul>SAP HANA Road Map: Renovation of DW and Innovation of Applications<br />
    36. 36. SAP HANA Road Map: Transformation of application platforms<br />Step 4 – Real Time Data Feed(2012/2013)<br />Applications write data simultaneously to traditional databases as well as the in-memory computing<br />Step 5 – Platform Consolidation<br /><ul><li>All applications (ERP and BW) run on data residing in-memory
    37. 37. Analytics and operations work on data in real time
    38. 38. In-memory computing executes all transactions, transformations, and complex data processing</li></li></ul><li>Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    39. 39. Real Time Enterprise: Value PropositionAddressing Key Business Drivers<br />Real-Time Decision Making<br />Fast and easy creation of ad-hoc views on business<br />Access to real time analysis<br />Accelerate Business Performance <br />Increase speed of transactional information flow in areas such as planning, forecasting, pricing, offers…<br />Unlock New Insights <br />Remove constraints for analyzing large data volumes - trends, data mining, predictive analytics etc.<br />Structured and unstructured data<br />Improve Business Productivity<br />Business designed and owned analytical models<br />Business self-service  reduce reliance on IT<br />Use data from anywhere<br />Improve IT efficiency<br />Manage growing data volume and complexity efficiently<br />Lower landscape costs <br />There is a significant interest from business to get agile analytic solutions.<br />„In a down economy, companies focus on cash protection. The decision on what needs to be done to make procurement more efficient is being made in the procurement department“.<br />CEO of a multinational transportation company<br />Flexibility to analyse business missed by LoB.<br />„First performance, and the other is flexibility on a business analyst level, who need to do deep diving to better understand and conclude. The second would be that also front-end tools are not providing flexibility“.<br />Executive of a global retail company<br />Traditional data warehouse processes are too complex and consume too much time for business departments.<br />„ The companies […] were frustrated with usual problems […] difficulty to build new information views. These companies were willing to move data […] into another proprietary file format […]. “<br />Analyst<br />
    40. 40. Real Time Enterprise: Value Proposition<br />The Value Blocks<br />Value Elements<br />In-Memory Enablers<br /><ul><li>Run performance-critical applications in-memory
    41. 41. Combine analytical and transactional applications
    42. 42. No need for planning levels or aggregation levels
    43. 43. Multi-dimensional simulation models updated in one step
    44. 44. Internal and external data securely combined
    45. 45. Batch data loads eliminated
    46. 46. New business models  based on real-time information and execution
    47. 47. Improved business agility  Dramatically improve planning, forecasting, price optimization and other processes
    48. 48. New business opportunities  faster, more accurate business decisions based on complex, large data volumes
    49. 49. High performance “real-time” analytics
    50. 50. Support for trending, simulation (“what-if”)
    51. 51. Business-driven data models
    52. 52. Support for structured and un-structured data
    53. 53. Analysis based on non-aggregated data sets
    54. 54. Sense and respond faster  Apply analytics to internal and external data in real-time to trigger actions (e.g., market analytics)
    55. 55. Business-driven “What-If”  Ask ad-hoc questions against the data set without IT
    56. 56. Right information at the right time
    57. 57. Eliminate BW database
    58. 58. Empower business self-service analytics – reduce shadow IT
    59. 59. Consolidate data warehouses and data marts
    60. 60. In-memory business applications (eliminate database for transactional systems)
    61. 61. Lower infrastructure costs  server, storage, database
    62. 62. Lower labor costs  backup/restore, reporting, performance tuning</li></li></ul><li>Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    63. 63. HANA Information Modeler<br />
    64. 64. HANA Information ModelerCreating Connectivity to a new system<br />
    65. 65. HANA Information ModelerCreating Attribute View<br />
    66. 66. HANA Information ModelerDefining Attributes (Key Attribute, Attribute, Filter and Measure (for numeric data types)<br />
    67. 67. HANA Information ModelerData Preview<br />
    68. 68. HANA Information ModelerCreating Hierarchies<br />
    69. 69. HANA Information ModelerCreating Analytic View<br />
    70. 70. HANA Information ModelerCreating Analytic View<br />
    71. 71. Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    72. 72. Connectivity from BO Enterprise Tools<br />Crystal Reports Enterprise - (ODBC, JDBC, Universe)<br />IDT (Information Design Tool) - JDBC<br />Explorer – Connection configuration in CMC<br />Advanced Analysis for Office (Q1 2011 release)<br />Web Intelligence – Universe<br />Xcelsius - Universe<br />
    73. 73. Agenda<br />Introduction to HANA: Vision and Strategy<br />Solution Overview & Roadmap<br />Business Value<br />HANA Modeling Studio<br />Connecting from BOE<br />Real time Examples<br />
    74. 74. Learning Resources<br />RKT Material<br />https://websmp208.sap-ag.de/rkt-hana<br />Navigation:<br />Consulting  SAP High-Performance Analytic Appliance 1.0  <br />Application Consultant s<br />Technology Consultants<br />
    75. 75. THANK YOU<br />

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